Seo Na Jin, Crocher Vincent, Spaho Egli, Ewert Charles R, Fathi Mojtaba F, Hur Pilwon, Lum Sara A, Humanitzki Elizabeth M, Kelly Abigail L, Ramakrishnan Viswanathan, Woodbury Michelle L
Na Jin Seo, PhD, is Associate Professor, Division of Occupational Therapy, Department of Health Professions, and Associate Professor, Department of Health Science and Research, Medical University of South Carolina, Charleston;
Vincent Crocher, PhD, is Research Associate, School of Engineering, University of Melbourne, Parkville, Victoria, Australia. At the time of the study, he was Postdoctoral Researcher, Department of Industrial and Manufacturing Engineering, University of Wisconsin-Milwaukee.
Am J Occup Ther. 2019 Jul/Aug;73(4):7304205090p1-7304205090p10. doi: 10.5014/ajot.2019.031682.
Along with growth in telerehabilitation, a concurrent need has arisen for standardized methods of tele-evaluation.
To examine the feasibility of using the Kinect sensor in an objective, computerized clinical assessment of upper limb motor categories.
We developed a computerized Mallet classification using the Kinect sensor. Accuracy of computer scoring was assessed on the basis of reference scores determined collaboratively by multiple evaluators from reviewing video recording of movements. In addition, using the reference score, we assessed the accuracy of the typical clinical procedure in which scores were determined immediately on the basis of visual observation. The accuracy of the computer scores was compared with that of the typical clinical procedure.
Research laboratory.
Seven patients with stroke and 10 healthy adult participants. Healthy participants intentionally achieved predetermined scores.
Accuracy of the computer scores in comparison with accuracy of the typical clinical procedure (immediate visual assessment).
The computerized assessment placed participants' upper limb movements in motor categories as accurately as did typical clinical procedures.
Computerized clinical assessment using the Kinect sensor promises to facilitate tele-evaluation and complement telehealth applications.
Computerized clinical assessment can enable patients to conduct evaluations remotely in their homes without therapists present.
随着远程康复的发展，同时出现了对标准化远程评估方法的需求。
探讨在对上肢运动类别进行客观的计算机化临床评估中使用Kinect传感器的可行性。
我们利用Kinect传感器开发了一种计算机化的槌状指分类法。计算机评分的准确性是根据由多名评估人员通过审查运动视频记录共同确定的参考分数来评估的。此外，我们使用参考分数评估了基于视觉观察立即确定分数的典型临床程序的准确性。将计算机评分的准确性与典型临床程序的准确性进行比较。
研究实验室。
7名中风患者和10名健康成年参与者。健康参与者有意达到预定分数。
将计算机评分的准确性与典型临床程序（即时视觉评估）的准确性进行比较。
计算机化评估将参与者的上肢运动准确地归类到运动类别中，与典型临床程序一样准确。
使用Kinect传感器进行计算机化临床评估有望促进远程评估并补充远程医疗应用。
计算机化临床评估可使患者在没有治疗师在场的情况下在家中远程进行评估。
